Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between members of a crowd, including any response to a vehicle. Our approach, Probabilistic Crowd GAN, extends recent work in trajectory prediction, combining Recurrent Neural Networks (RNNs) with Mixture Density Networks (MDNs) to output probabilistic multimodal predictions, from which likely modal paths are found and used for adversarial training. We also propose the use of Graph Vehicle-Pedestrian Attention Network (GVAT), which models social interactions and allows input of a shared vehicle feature, showing that inclusion of this module leads to improved trajectory prediction both with and without the presence of a vehicle. Through evaluation on various datasets, we demonstrate improvements on the existing state of the art methods for trajectory prediction and illustrate how the true multimodal and uncertain nature of crowd interactions can be directly modelled.

Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions of the pedestrians’ future state as human motion prediction has a large variance. To overcome this problem, we propose a new method, SARL-SGAN-KCE, that combines a deep socially aware attentive value network with a human multimodal trajectory prediction model to help identify the optimal driving policy. We also introduce a novel technique to extend the discrete action space with minimal additional computational requirements. The kinematic constraints of the vehicle are also considered to ensure smooth and safe trajectories. We evaluate our method against the state of art methods for crowd navigation and provide an ablation study to show that our method is safer and closer to human behaviour.

High-definition (HD) maps aim to provide detailed road information with centimeter-level accuracy, essential for enabling precise navigation and safe operation of autonomous vehicles (AVs). Traditional offline construction methods involve several complex steps, such as data collection, point cloud generation, and feature extraction, but these methods are resource-intensive and struggle to keep pace with the rapidly changing road environments. In contrast, online HD map construction leverages onboard sensor data to dynamically generate local HD maps, offering a bird’s-eye view (BEV) representation of the surrounding road environment. This approach has the potential to improve adaptability to spatial and temporal changes in road conditions while enhancing cost-efficiency by reducing the dependency on frequent map updates and expensive survey fleets. This survey provides a comprehensive analysis of online HD map construction, including the task background, high-level motivations, research methodology, key advancements, existing challenges, and future trends. We systematically review the latest advancements in three key sub-tasks: map segmentation, map element detection, and lane graph construction, aiming to bridge gaps in the current literature. We also discuss existing challenges and future trends, covering standardized map representation design, multitask learning, and multi-modality fusion, while offering suggestions for potential improvements.

Traffic accidents, including animal-vehicle collisions (AVCs), endanger both humans and wildlife. This letter presents an innovative self-training methodology aimed at detecting rare animals, such as cassowaries in Australia, whose survival is threatened by road accidents. The proposed method addresses critical real-world challenges, including the acquisition and labelling of sensor data for rare animal species in resource-limited environments. It achieves this by leveraging cloud and edge computing, and automatic data labelling to improve the detection performance of the field-deployed model iteratively. Our approach introduces Label-Augmentation Non-Maximum Suppression (LA-NMS), which incorporates a vision-language model (VLM) to enable automated data labelling. During a five-month deployment, we confirmed the robustness and effectiveness of the method, achieving improved object detection accuracy and increased prediction confidence.

Based on the power marketing system, this work investigates and constructs the data quality verification service including three functional modules: verification engine, verification management and verification application, and carries out data quality verification on the topic of electricity bill accounting. The results indicate that the data quality verification service based on power marketing system is scientific and complete. The verification results are accurate, and the verification efficiency is high. The data quality of electricity bill accounting topics performs well in the prefecture and professional dimensions, meeting the standard of 95% data compliance rate. Abnormal data is mainly caused by human factor, data migration factor, and data updates factor.

High-Definition (HD) maps aim to provide comprehensive road information with centimeter-level accuracy, essential for precise navigation and safe operation of Autonomous Vehicles (AVs). Traditional offline construction methods involve multiple complex steps—such as data collection, point cloud map generation, and feature extraction—which not only incur high costs but also struggle to adapt to the rapid changes in dynamic environments. In contrast, the online construction method utilizes onboard sensor data to dynamically generate local HD maps, providing a bird’s-eye view (BEV) representation of the surrounding road environment. This approach has the potential for superior scalability and adaptability, enabling more effective responses to the ever-evolving road conditions. In this survey, we examine the latest advancements in online HD map construction, aiming to bridge the gaps in existing literature. We analyze key advancements, methodologies, advantages, and disadvantages, along with future research directions.